The Importance of Subjective Cognitive Decline Recognition and the Potential of Molecular and Neurophysiological Biomarkers-A Systematic Review

Screening tools for subjective cognitive decline and mild cognitive impairment based on task-state prefrontal functional connectivity: a functional near-infrared spectroscopy study

BACKGROUND

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) carry the risk of progression to dementia, and accurate screening methods for these conditions are urgently needed. Studies have suggested the potential ability of functional near-infrared spectroscopy (fNIRS) to identify MCI and SCD. The present fNIRS study aimed to develop an early screening method for SCD and MCI based on activated prefrontal functional connectivity (FC) during the performance of cognitive scales and subject-wise cross-validation via machine learning.

METHODS

Activated prefrontal FC data measured by fNIRS were collected from 55 normal controls, 80 SCD patients, and 111 MCI patients. Differences in FC were analyzed among the groups, and FC strength and cognitive scale performance were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95 % confidence interval (CI) values.

RESULTS

Statistical analysis revealed a trend toward more impaired prefrontal FC with declining cognitive function. Prediction models were built by combining features of prefrontal FC and cognitive scale performance and applying machine learning models, The models showed generally satisfactory abilities to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 92.0 % for MCI vs. NC, 80.0 % for MCI vs. SCD, and 76.1 % for SCD vs. NC were achieved, and the highest AUC values were 97.0 % (95 % CI: 94.6 %-99.3 %) for MCI vs. NC, 87.0 % (95 % CI: 81.5 %-92.5 %) for MCI vs. SCD, and 79.2 % (95 % CI: 71.0 %-87.3 %) for SCD vs. NC.

CONCLUSION

The developed screening method based on fNIRS and machine learning has the potential to predict early-stage cognitive impairment based on prefrontal FC data collected during cognitive scale-induced activation.

Exploring predementia: Understanding the characteristics of subjective cognitive decline plus from India

BACKGROUND

Subjective cognitive decline (SCD) is the early predementia syndrome. that occurs even before the development of objective cognitive decline. SCD plus refers to an additional set of criteria that increases the likelihood of developing mild cognitive impairment and further progressing to Alzheimer's disease (AD). Studying the progression of SCD-plus participants will help in understanding the importance of diagnosing this condition at an early stage and delaying its onset.

OBJECTIVE

The present tries to examine neurocognitive changes in individuals who met the criteria of SCD-plus patients. The study also investigated the imaging correlates of these individuals in both cohorts.

METHODS

This study included 94 participants from Srinivaspura Aging, Neuro Senescence, and COGnition (SANSCOG) and Tata Longitudinal Study of Aging (TLSA) cohorts who satisfy the criteria of SCD plus. Mann-Whitney U test was used to compare the SCD plus participants and healthy controls. Regression analysis was performed to find the association between SCD plus and cognition.

RESULTS

The SCD-plus group performed poorer than the healthy group in episodic memory delayed recall (p = 0.049), name face recognition (p = 0.023), and letter fluency (p = 0.004) tasks. The generalized linear model revealed that the SCD-plus group had lower left cerebellar cortex (p = 0.010) and right inferior occipital cortex (p = 0.016) volumes than the healthy control group.

CONCLUSIONS

The participants in the SCD-plus group performed poorly on memory and language-related tasks, and the volumes of the associated brain regions decreased. This study suggested that the SCD-plus group had characteristics similar to AD group and can help in identifying AD at the earliest.

An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy

Purpose

Functional near-infrared spectroscopy (fNIRS) has shown feasibility in evaluating cognitive function and brain functional connectivity (FC). Therefore, this fNIRS study aimed to develop a screening method for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) based on resting-state prefrontal FC and neuropsychological tests via machine learning.

Methods

Functional connectivity data measured by fNIRS were collected from 55 normal controls (NCs), 80 SCD individuals, and 111 MCI individuals. Differences in FC were analyzed among the groups. FC strength and neuropsychological test scores were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95% confidence interval (CI) values.

Results

Statistical analysis revealed a trend toward compensatory enhanced prefrontal FC in SCD and MCI individuals. The models showed a satisfactory ability to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 94.9% for MCI vs. NC, 79.4% for MCI vs. SCD, and 77.0% for SCD vs. NC were achieved, and the highest AUC values were 97.5% (95% CI: 95.0%-100.0%) for MCI vs. NC, 83.7% (95% CI: 77.5%-89.8%) for MCI vs. SCD, and 80.6% (95% CI: 72.7%-88.4%) for SCD vs. NC.

Conclusion

The developed screening method based on resting-state prefrontal FC measured by fNIRS and machine learning may help predict early-stage cognitive impairment.

Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment

Introduction

Alzheimer's disease (AD) is a common neurological disorder. Based on clinical characteristics, it can be categorized into normal cognition (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia (AD). Once the condition begins to progress, the process is usually irreversible. Therefore, early identification and intervention are crucial for patients. This study aims to explore the sensitivity of fNIRS in distinguishing between SCD and MCI.

Methods

An in-depth analysis of the Functional Connectivity (FC) and oxygenated hemoglobin (HbO) characteristics during resting state and different memory cognitive tasks is conducted on two patient groups to search for potential biomarkers. The 33 participants were divided into two groups: SCD and MCI.

Results

Functional connectivity strength during the resting state and hemodynamic changes during the execution of Verbal Fluency Tasks (VFT) and MemTrax tasks were measured using fNIRS. The results showed that compared to individuals with MCI, patients with SCD exhibited higher average FC levels between different channels in the frontal lobe during resting state, with two channels' FC demonstrating significant ability to distinguish between SCD and MCI. During the VFT task, the overall average HbO concentration in the frontal lobe of SCD patients was higher than that of MCI patients from 5 experimental paradigm. Receiver operating characteristic analysis indicated that the accuracy of the above features in distinguishing SCD from MCI was 78.8%, 72.7%, 75.8%, and 66.7%, respectively.

Discussion

fNIRS could potentially serve as a non-invasive biomarker for the early detection of dementia.

Adaptive spatiotemporal encoding network for cognitive assessment using resting state EEG

Cognitive impairment, marked by neurodegenerative damage, leads to diminished cognitive function decline. Accurate cognitive assessment is crucial for early detection and progress evaluation, yet current methods in clinical practice lack objectivity, precision, and convenience. This study included 743 participants, including healthy individuals, mild cognitive impairment (MCI), and dementia patients, with collected resting-state EEG data and cognitive scale scores. An adaptive spatiotemporal encoding framework was developed based on resting-state EEG, achieving an MAE of 3.12% (95% CI: 2.9034, 3.3975) in testing (sensitivity: 0.97, 95% CI: 0.779,1; specificity: 0.97, 95% CI: 0.779,1). The model's effectiveness was also validated on the neurofeedback (sensitivity: 0.867, 95% CI: 0.621, 0.963; specificity: 1, 95% CI: 0.439, 1.0) and TMS datasets (sensitivity: 0.833, 95% CI: 0.608, 0.942), which effectively reflect the participants' cognitive changes. The model effectively extracted repetitive spatiotemporal patterns from resting-state EEG, aiding in cognitive disease diagnosis and assessment in various scenarios.

Neurophysiological markers of early cognitive decline in older adults: a mini-review of electroencephalography studies for precursors of dementia

The early detection of cognitive decline in older adults is crucial for preventing dementia. This mini-review focuses on electroencephalography (EEG) markers of early dementia-related precursors, including subjective cognitive decline, subjective memory complaints, and cognitive frailty. We present recent findings from EEG analyses identifying high dementia risk in older adults, with an emphasis on conditions that precede mild cognitive impairment. We also cover event-related potentials, quantitative EEG markers, microstate analysis, and functional connectivity approaches. Moreover, we discuss the potential of these neurophysiological markers for the early detection of cognitive decline as well as their correlations with related biomarkers. The integration of EEG data with advanced artificial intelligence technologies also shows promise for predicting the trajectory of cognitive decline in neurodegenerative disorders. Although challenges remain in its standardization and clinical application, EEG-based approaches offer non-invasive, cost-effective methods for identifying individuals at risk of dementia, which may enable earlier interventions and personalized treatment strategies.

Herpes zoster and long-term risk of subjective cognitive decline

BACKGROUND

Herpes zoster (HZ), commonly known as "shingles," may contribute to cognitive decline through mechanisms such as neuroinflammation or direct neuronal injury. However, evidence on the longitudinal association between HZ and cognitive decline is conflicting and whether the risk differs by APOE ε4-carrier status has not been studied; prospective cohort studies on the association between HZ vaccination and cognitive decline are also lacking.

METHODS

We included 149,327 participants from three large cohorts-the Nurses' Health Study (NHS), NHSII, and Health Professionals Follow-Up Study (HPFS)-to prospectively examine the association between HZ and subsequent subjective cognitive decline (SCD). Poisson regression was used to estimate the multivariable-adjusted relative risk (MVRR) of a 3-unit increment in SCD score according to years since HZ compared with participants with no history of HZ.

RESULTS

Compared with individuals with no history of HZ, the MVRR (95% CI) of a 3-unit increment in SCD score was significantly and independently higher among individuals with a history of HZ, but the duration of time since HZ when the elevated risk of SCD was statistically significant differed among the cohorts. In NHS, HZ was associated with higher long-term risk of SCD; compared with individuals with no history of HZ, the MVRR (95% CI) of a 3-unit increment in SCD score was 1.14 (1.01, 1.32) for ≥ 13 years since HZ. In NHS II, HZ was associated with higher risk of SCD in both the short-term [MVRR 1.34 (1.18, 1.53) for 1-4 years] and long-term [MVRR 1.20 (1.08, 1.34) for ≥ 13 years since HZ]. In HPFS, an elevated risk of SCD was suggested across all time points. Among the subset of participants with information on APOE ε4, there was a suggestion that the association differed by APOE ε4 carrier status, but the results were not consistent between women and men. Among the subset of women with information on HZ vaccination, there was a suggestion that the long-term risk of SCD may be greater among women who were not vaccinated against HZ.

CONCLUSIONS

Data from three large independent cohorts of women and men showed that HZ was associated with higher long-term risk of SCD, and the risk may differ by APOE ε4-carrier status.

Changes in cognitive ability and serum microRNA levels during aging in mice

Mild cognitive impairment (MCI) is an early stage that can result in dementia. MCI can be reversed, and diagnosis at an early stage is crucial to control the progression to dementia. Dementia is currently diagnosed based on interviews and screening tests; however, novel biomarkers must be identified to allow early MCI detection. Therefore, the present study aimed to identify novel biomarkers in the form of blood microRNAs (miRNAs/miRs) for the diagnosis of MCI or early dementia. Blood samples were collected from C57BL/6NJcl male mice at four time points, including 4-week-old (4W), 8-week-old (8W), 36-week-old (36W) and 58-week-old (58W), and serum was isolated. Body weight and blood total cholesterol levels were increased, and blood alkaline phosphatase was decreased with aging. The 8W mice exhibited the highest cognitive ability in the Morris water maze test, whereas the 58W mice demonstrated decreased cognitive ability. The serum RNA concentrations of the 4W, 8W, 36W and 58W mice demonstrated no significant differences. Furthermore, small RNA levels were detected in the serum of all mice. miRNA microarray analysis revealed a >1.5-fold increase in the serum expression of two miRNAs (miR-21a-5p and miR-92a-3p) and a >1.5-fold decrease in the serum expression of two other miRNAs (miR-6769b-5p and miR-709) in 58W mice compared with those in 8W mice. In the future, we aim to further analyze aged mice to discover novel MCI biomarkers.